Campfire Session — Computer Science

Discover how teachers and students use AI to transform Computer Science learning with Flint with tutors, simulations, analytics, and student-created activities.

Lulu Gao, Head of Teacher Experience at Flint | LinkedIn

In this Campfire Session, our team explored how computer science teachers and students are using Flint to make CS more independent, engaging, and inclusive. We heard real classroom stories about running simultaneous AP courses with AI tutors, supporting multilingual learners with custom companions, and using analytics to see exactly where students are stuck. We also walked through how to turn a simple brainstorming chat into a full Flint activity, and heard from a student who built her own debugging “dungeon” game to make error-hunting less painful and more fun.

Content covered in this session includes:

• Introductions, community updates, and industry context on how professional developers are using AI
• Teacher share-outs on using Flint for AP CSA, AP Networking, pseudocode, and software development lifecycle simulations
• Using Flint chats and activities to design CS learning experiences (e.g., sorting/searching playlist project)
• Practical tips for activity creation, iteration, and leveraging analytics to target support
• Student perspective on building custom Flint activities for debugging, assignment unpacking, and confidence-building in CS

Slides from the presentation can be found here.

Got more questions, comments, or feedback for this topic? Feel free to raise them within the Flint Community.

Join our next Campfire Session 🏕️

Subscribe to our events calendar to be notified when upcoming Campfire Sessions and other Flint events get scheduled.

See Events Calendar

Introduction • 00:00

• Lulu introduces the session and agenda.

Ice-breaking news • 01:26

• Lulu presents an icebreaking update referencing a 2025 August study showing senior developers ship significantly more AI-generated code, discusses confidence in catching AI mistakes and the impact on education strategies for teaching AI use in computer science.

Spencer Wagner's computer science case study • 04:53

• Spencer Wagner describes using Flint for self-paced, independent coding lessons across AP Computer Science and CSA courses, enabling simultaneous teaching of multiple groups and frequent tutor support.
• Spencer Wagner notes improved AP outcomes and benefits of AI-assisted feedback, including higher percentages of high AP scores and expanded use beyond multiple choice questions.
• Lulu Gao acknowledges Spencer Wagner's results, and prepares to pass the discussion to Craig Griebenow from Shanghai Singapore International School, inviting further questions.

Craig Griebenow's computer science case study • 11:23

• A group discusses integrating AI into classroom activities, sharing a hands-on prompt-engineering exercise where students act as prompters and iteratively improve drawings with feedback.
• The participants describe ethical guidance and encouraging responsible AI use, including a house-point visualization project and HTML/web skills demonstrations.
• Dylan asks about mechanisms used to switch from general tools to Flint, mentioning incentives, guardrails, and monitoring; Spencer explains using in-class assessments, pre-made practice questions, and AI for tutoring while checking logs.
• The speaker demonstrates building an interactive HTML project with a grade-eight student, including loading an image, adding navigation, and generating a dynamic, zero-code webpage as a learning milestone.
• The teacher discusses creating multiple tutors in Flint to support students learning in their preferred programming language and at individual paces, with immediate feedback and progress tracking.

How to use Flint for computer science • 35:06

• Introduction to several product updates: base LLM updated to Claude Sonet 4.5, improved reasoning and step-by-step guidance; added Google Drive and OneDrive integration; attention alerts for teachers; visibility of student engagement and conduct during roaming demonstrations.
• Sparky's response length can be adjusted for younger students, making it more concise as requested.
• Clarification that Flint cannot run code yet but can evaluate it for bugs and efficiency; no built-in sandbox currently, with plans for future exploration.
• Demonstration of using chats to brainstorm activities, generating data sets, and creating guided Flint activities with predefined settings and prompts; workflow includes uploading datasets and instruction files to create new activities.

Student shareout • 46:29

• Izzy describes using Flint AI for CS practice problems and extends learning beyond the class to fit her learning style.
• She shares a debugging dungeon activity to input code and guide peers through fixing errors.
• Izzy highlights building a help activity that interprets and explains assignments, aiding understanding of concepts.

Conclusion • 50:42

• Discussion centers on analytics power and impact on teaching, with notes on granular student insights and potential to tailor support.
• Lulu shares QR codes for people to check out the Campfire Calendar, Flint's Instagram (which has a bunch of teacher-facing content), and the Flint Community.